Oil tail risks and the forecastability of the realized variance of oil-price: Evidence from over 150 years of data

Afees Salisu, Christian Pierdzioch and Rangan Gupta

Finance Research Letters, 2022, vol. 46, issue PB

Abstract: We examine the predictive value of tail risks of oil returns for the realized variance of oil returns using monthly data for the modern oil industry (1859:10–2020:10). The Conditional Autoregressive Value at Risk (CAViaR) framework is employed to generate the tail risks for both 1% and 5% VaRs across four variants of the CAViaR framework. We find evidence of both in-sample and out-of-sample predictability emanating from both 1% and 5% tail risks. Given the importance of real-time oil-price volatility forecasts, our results have important implications for investors and policymakers.

Keywords: Oil tail risks; Realized variance of oil-price; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 Q02 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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Working Paper: Oil Tail Risks and the Forecastability of the Realized Variance of Oil-Price: Evidence from Over 150 Years of Data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:46:y:2022:i:pb:s1544612321003809

DOI: 10.1016/j.frl.2021.102378

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